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Found 776 Skills
Optimize web application performance using code splitting, lazy loading, caching, compression, and monitoring. Use when improving Core Web Vitals and user experience.
Comprehensive PostgreSQL database engineering skill covering indexing strategies, query optimization, performance tuning, partitioning, replication, backup and recovery, high availability, and production database management. Master advanced PostgreSQL features including MVCC, VACUUM operations, connection pooling, monitoring, and scalability patterns.
Configure Sentry for error tracking, performance monitoring, and log aggregation. Integrates with Pino to forward logs to Sentry automatically.
AWS CloudWatch monitoring for logs, metrics, alarms, and dashboards. Use when setting up monitoring, creating alarms, querying logs with Insights, configuring metric filters, building dashboards, or troubleshooting application issues.
AWS RDS (Relational Database Service) management using AWS SDK for Java 2.x. Use when creating, modifying, monitoring, or managing Amazon RDS database instances, snapshots, parameter groups, and configurations.
Implements standardized API error responses with proper status codes, logging, and user-friendly messages. Use when building production APIs, implementing error recovery patterns, or integrating error monitoring services.
Expert in Machine Learning Operations bridging data science and DevOps. Use when building ML pipelines, model versioning, feature stores, or production ML serving. Triggers include "MLOps", "ML pipeline", "model deployment", "feature store", "model versioning", "ML monitoring", "Kubeflow", "MLflow".
The systematic orchestration of AI-powered marketing workflows that combine content generation, approval processes, multi-channel distribution, and quality gates into cohesive automation systems. This skill integrates AI generation tools (Jasper, Claude, GPT) with automation platforms (Zapier, Make, n8n) and marketing systems to build scalable content pipelines. It focuses on maintaining brand consistency, implementing rigorous quality gates, and balancing automation with strategic human oversight. Key capabilities include designing parallel approval flows, monitoring costs, and architecting "invisible" automation that enhances productivity without sacrificing quality.Use when "AI workflow, automate content, content automation, workflow automation, AI pipeline, automated marketing, content distribution automation, approval workflow, scale content production, AI orchestration, automation, workflow, ai-orchestration, content-pipeline, approval-workflow, multi-channel, quality-gates, cost-control" mentioned.
Setup Sentry in React Native using the wizard CLI. Use when asked to add Sentry to React Native, install @sentry/react-native, or configure error monitoring for React Native or Expo apps.
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
Migration monitoring, CDC, and observability infrastructure
This skill should be used when the user asks to "fetch Sentry issues", "check Sentry errors", "triage Sentry", "categorize Sentry issues", "resolve Sentry issue", "mute Sentry issue", "unresolve Sentry issue", "sentry-cli", or mentions Sentry API, Sentry project issues, error monitoring, issue triage, Sentry stack traces, or browser extension errors in Sentry.